Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.1 KiB
Average record size in memory655.9 B

Variable types

Categorical10
Text1
Numeric17

Alerts

Revenue diff has constant value "0.0"Constant
Expected revenue is highly overall correlated with Number of products sold and 2 other fieldsHigh correlation
Number of products sold is highly overall correlated with Expected revenue and 1 other fieldsHigh correlation
Price is highly overall correlated with Expected revenue and 1 other fieldsHigh correlation
Revenue generated is highly overall correlated with Expected revenue and 2 other fieldsHigh correlation
Stock levels is highly overall correlated with Stock levels (adj)High correlation
Stock levels (adj) is highly overall correlated with Stock levels and 1 other fieldsHigh correlation
stock_violation is highly overall correlated with Stock levels (adj)High correlation
stock_violation is highly imbalanced (91.9%)Imbalance
SKU has unique valuesUnique
Price has unique valuesUnique
Revenue generated has unique valuesUnique
Shipping costs has unique valuesUnique
Manufacturing costs has unique valuesUnique
Costs has unique valuesUnique
Expected revenue has unique valuesUnique

Reproduction

Analysis started2025-10-20 13:14:21.170791
Analysis finished2025-10-20 13:14:51.549968
Duration30.38 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Product type
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
skincare
40 
haircare
34 
cosmetics
26 

Length

Max length9
Median length8
Mean length8.26
Min length8

Characters and Unicode

Total characters826
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhaircare
2nd rowskincare
3rd rowhaircare
4th rowskincare
5th rowskincare

Common Values

ValueCountFrequency (%)
skincare40
40.0%
haircare34
34.0%
cosmetics26
26.0%

Length

2025-10-20T13:14:51.665690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:51.744849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
skincare40
40.0%
haircare34
34.0%
cosmetics26
26.0%

Most occurring characters

ValueCountFrequency (%)
c126
15.3%
a108
13.1%
r108
13.1%
i100
12.1%
e100
12.1%
s92
11.1%
n40
 
4.8%
k40
 
4.8%
h34
 
4.1%
o26
 
3.1%
Other values (2)52
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c126
15.3%
a108
13.1%
r108
13.1%
i100
12.1%
e100
12.1%
s92
11.1%
n40
 
4.8%
k40
 
4.8%
h34
 
4.1%
o26
 
3.1%
Other values (2)52
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c126
15.3%
a108
13.1%
r108
13.1%
i100
12.1%
e100
12.1%
s92
11.1%
n40
 
4.8%
k40
 
4.8%
h34
 
4.1%
o26
 
3.1%
Other values (2)52
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c126
15.3%
a108
13.1%
r108
13.1%
i100
12.1%
e100
12.1%
s92
11.1%
n40
 
4.8%
k40
 
4.8%
h34
 
4.1%
o26
 
3.1%
Other values (2)52
6.3%

SKU
Text

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2025-10-20T13:14:52.041518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9
Min length4

Characters and Unicode

Total characters490
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowSKU0
2nd rowSKU1
3rd rowSKU2
4th rowSKU3
5th rowSKU4
ValueCountFrequency (%)
sku01
 
1.0%
sku11
 
1.0%
sku21
 
1.0%
sku31
 
1.0%
sku41
 
1.0%
sku51
 
1.0%
sku61
 
1.0%
sku71
 
1.0%
sku81
 
1.0%
sku91
 
1.0%
Other values (90)90
90.0%
2025-10-20T13:14:52.430156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S100
20.4%
K100
20.4%
U100
20.4%
120
 
4.1%
220
 
4.1%
620
 
4.1%
320
 
4.1%
420
 
4.1%
520
 
4.1%
820
 
4.1%
Other values (3)50
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S100
20.4%
K100
20.4%
U100
20.4%
120
 
4.1%
220
 
4.1%
620
 
4.1%
320
 
4.1%
420
 
4.1%
520
 
4.1%
820
 
4.1%
Other values (3)50
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S100
20.4%
K100
20.4%
U100
20.4%
120
 
4.1%
220
 
4.1%
620
 
4.1%
320
 
4.1%
420
 
4.1%
520
 
4.1%
820
 
4.1%
Other values (3)50
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S100
20.4%
K100
20.4%
U100
20.4%
120
 
4.1%
220
 
4.1%
620
 
4.1%
320
 
4.1%
420
 
4.1%
520
 
4.1%
820
 
4.1%
Other values (3)50
10.2%

Price
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.462461
Minimum1.699976
Maximum99.171329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:52.557070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.699976
5-th percentile4.0507218
Q119.597823
median51.239831
Q377.198228
95-th percentile96.396366
Maximum99.171329
Range97.471353
Interquartile range (IQR)57.600405

Descriptive statistics

Standard deviation31.168193
Coefficient of variation (CV)0.63013833
Kurtosis-1.3734706
Mean49.462461
Median Absolute Deviation (MAD)28.292667
Skewness-0.022538919
Sum4946.2461
Variance971.45624
MonotonicityNot monotonic
2025-10-20T13:14:52.692511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.808005541
 
1.0%
14.843523281
 
1.0%
11.319683291
 
1.0%
61.163343021
 
1.0%
4.8054960361
 
1.0%
1.6999760141
 
1.0%
4.0783328631
 
1.0%
42.958384381
 
1.0%
68.717596751
 
1.0%
64.015732941
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
1.6999760141
1.0%
2.3972747061
1.0%
3.0376887251
1.0%
3.1700114141
1.0%
3.5261112591
1.0%
4.0783328631
1.0%
4.1563083591
1.0%
4.3243411861
1.0%
4.8054960361
1.0%
6.3068831761
1.0%
ValueCountFrequency (%)
99.171328641
1.0%
99.113291621
1.0%
98.031829661
1.0%
97.760085581
1.0%
97.446946621
1.0%
96.341072441
1.0%
95.712135881
1.0%
92.996884231
1.0%
92.557360811
1.0%
91.128318351
1.0%

Availability
Real number (ℝ)

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.4
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:52.819808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q122.75
median43.5
Q375
95-th percentile96.05
Maximum100
Range99
Interquartile range (IQR)52.25

Descriptive statistics

Standard deviation30.743317
Coefficient of variation (CV)0.63519249
Kurtosis-1.3319932
Mean48.4
Median Absolute Deviation (MAD)27.5
Skewness0.18361821
Sum4840
Variance945.15152
MonotonicityNot monotonic
2025-10-20T13:14:52.949332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114
 
4.0%
343
 
3.0%
553
 
3.0%
263
 
3.0%
233
 
3.0%
903
 
3.0%
753
 
3.0%
293
 
3.0%
163
 
3.0%
563
 
3.0%
Other values (53)69
69.0%
ValueCountFrequency (%)
12
2.0%
31
 
1.0%
52
2.0%
61
 
1.0%
92
2.0%
102
2.0%
114
4.0%
121
 
1.0%
131
 
1.0%
142
2.0%
ValueCountFrequency (%)
1001
 
1.0%
991
 
1.0%
981
 
1.0%
972
2.0%
961
 
1.0%
952
2.0%
941
 
1.0%
932
2.0%
911
 
1.0%
903
3.0%

Number of products sold
Real number (ℝ)

High correlation 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.99
Minimum8
Maximum996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:53.083519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile60.5
Q1184.25
median392.5
Q3704.25
95-th percentile960.15
Maximum996
Range988
Interquartile range (IQR)520

Descriptive statistics

Standard deviation303.78007
Coefficient of variation (CV)0.65897324
Kurtosis-1.2513936
Mean460.99
Median Absolute Deviation (MAD)241.5
Skewness0.28141802
Sum46099
Variance92282.333
MonotonicityNot monotonic
2025-10-20T13:14:53.209944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3362
 
2.0%
3202
 
2.0%
1342
 
2.0%
9632
 
2.0%
8711
 
1.0%
1471
 
1.0%
81
 
1.0%
8021
 
1.0%
4261
 
1.0%
1501
 
1.0%
Other values (86)86
86.0%
ValueCountFrequency (%)
81
1.0%
241
1.0%
251
1.0%
291
1.0%
321
1.0%
621
1.0%
651
1.0%
791
1.0%
831
1.0%
931
1.0%
ValueCountFrequency (%)
9961
1.0%
9871
1.0%
9801
1.0%
9632
2.0%
9601
1.0%
9461
1.0%
9331
1.0%
9191
1.0%
9161
1.0%
9131
1.0%

Revenue generated
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22856.977
Minimum90.557466
Maximum87098.044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:53.333347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90.557466
5-th percentile865.77599
Q15489.6645
median13134.49
Q337071.66
95-th percentile63355.905
Maximum87098.044
Range87007.486
Interquartile range (IQR)31581.995

Descriptive statistics

Standard deviation22844.682
Coefficient of variation (CV)0.9994621
Kurtosis0.31115386
Mean22856.977
Median Absolute Deviation (MAD)10132.709
Skewness1.1252807
Sum2285697.7
Variance5.2187949 × 108
MonotonicityNot monotonic
2025-10-20T13:14:53.464369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55986.020441
 
1.0%
10924.833131
 
1.0%
90.557466341
 
1.0%
5076.557471
 
1.0%
4185.5870481
 
1.0%
249.8964741
 
1.0%
265.09163611
 
1.0%
18300.271751
 
1.0%
10307.639511
 
1.0%
62735.418281
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
90.557466341
1.0%
218.61889811
1.0%
249.8964741
1.0%
265.09163611
1.0%
810.81931281
1.0%
868.66844711
1.0%
944.5262341
1.0%
1533.2653381
1.0%
1690.8174041
1.0%
1924.074861
1.0%
ValueCountFrequency (%)
87098.043651
1.0%
87010.041581
1.0%
80386.100321
1.0%
79463.89361
1.0%
75145.148751
1.0%
62735.418281
1.0%
61862.960031
1.0%
56894.560361
1.0%
56362.066161
1.0%
55986.020441
1.0%
Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Unknown
31 
Female
25 
Non-binary
23 
Male
21 

Length

Max length10
Median length7
Mean length6.81
Min length4

Characters and Unicode

Total characters681
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-binary
2nd rowFemale
3rd rowUnknown
4th rowNon-binary
5th rowNon-binary

Common Values

ValueCountFrequency (%)
Unknown31
31.0%
Female25
25.0%
Non-binary23
23.0%
Male21
21.0%

Length

2025-10-20T13:14:53.579964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:53.654895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown31
31.0%
female25
25.0%
non-binary23
23.0%
male21
21.0%

Most occurring characters

ValueCountFrequency (%)
n139
20.4%
e71
10.4%
a69
10.1%
o54
 
7.9%
l46
 
6.8%
U31
 
4.6%
w31
 
4.6%
k31
 
4.6%
F25
 
3.7%
m25
 
3.7%
Other values (7)159
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n139
20.4%
e71
10.4%
a69
10.1%
o54
 
7.9%
l46
 
6.8%
U31
 
4.6%
w31
 
4.6%
k31
 
4.6%
F25
 
3.7%
m25
 
3.7%
Other values (7)159
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n139
20.4%
e71
10.4%
a69
10.1%
o54
 
7.9%
l46
 
6.8%
U31
 
4.6%
w31
 
4.6%
k31
 
4.6%
F25
 
3.7%
m25
 
3.7%
Other values (7)159
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n139
20.4%
e71
10.4%
a69
10.1%
o54
 
7.9%
l46
 
6.8%
U31
 
4.6%
w31
 
4.6%
k31
 
4.6%
F25
 
3.7%
m25
 
3.7%
Other values (7)159
23.3%

Stock levels
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.27
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:53.763534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117.75
median48
Q373
95-th percentile97
Maximum100
Range99
Interquartile range (IQR)55.25

Descriptive statistics

Standard deviation30.996531
Coefficient of variation (CV)0.64214898
Kurtosis-1.2117776
Mean48.27
Median Absolute Deviation (MAD)27
Skewness0.093650625
Sum4827
Variance960.78495
MonotonicityNot monotonic
2025-10-20T13:14:53.894727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55
 
5.0%
904
 
4.0%
483
 
3.0%
1003
 
3.0%
103
 
3.0%
43
 
3.0%
312
 
2.0%
932
 
2.0%
12
 
2.0%
542
 
2.0%
Other values (55)71
71.0%
ValueCountFrequency (%)
12
 
2.0%
21
 
1.0%
43
3.0%
55
5.0%
61
 
1.0%
91
 
1.0%
103
3.0%
111
 
1.0%
121
 
1.0%
132
 
2.0%
ValueCountFrequency (%)
1003
3.0%
981
 
1.0%
972
2.0%
962
2.0%
951
 
1.0%
932
2.0%
921
 
1.0%
904
4.0%
891
 
1.0%
861
 
1.0%

Lead times
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.96
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:54.008785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median17
Q324
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.7858012
Coefficient of variation (CV)0.5504888
Kurtosis-1.1888488
Mean15.96
Median Absolute Deviation (MAD)8
Skewness-0.12983854
Sum1596
Variance77.190303
MonotonicityNot monotonic
2025-10-20T13:14:54.127474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
276
 
6.0%
176
 
6.0%
16
 
6.0%
85
 
5.0%
235
 
5.0%
195
 
5.0%
265
 
5.0%
255
 
5.0%
295
 
5.0%
114
 
4.0%
Other values (19)48
48.0%
ValueCountFrequency (%)
16
6.0%
23
3.0%
31
 
1.0%
42
 
2.0%
54
4.0%
62
 
2.0%
73
3.0%
85
5.0%
92
 
2.0%
103
3.0%
ValueCountFrequency (%)
302
 
2.0%
295
5.0%
281
 
1.0%
276
6.0%
265
5.0%
255
5.0%
243
3.0%
235
5.0%
222
 
2.0%
202
 
2.0%

Order quantities
Real number (ℝ)

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.22
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:54.255767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q126
median52
Q371.25
95-th percentile88
Maximum96
Range95
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation26.784429
Coefficient of variation (CV)0.54417776
Kurtosis-1.1192733
Mean49.22
Median Absolute Deviation (MAD)23.5
Skewness-0.10737313
Sum4922
Variance717.40566
MonotonicityNot monotonic
2025-10-20T13:14:54.392675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
856
 
6.0%
664
 
4.0%
724
 
4.0%
523
 
3.0%
113
 
3.0%
273
 
3.0%
263
 
3.0%
513
 
3.0%
223
 
3.0%
802
 
2.0%
Other values (51)66
66.0%
ValueCountFrequency (%)
11
 
1.0%
21
 
1.0%
41
 
1.0%
61
 
1.0%
72
2.0%
81
 
1.0%
92
2.0%
102
2.0%
113
3.0%
151
 
1.0%
ValueCountFrequency (%)
962
 
2.0%
951
 
1.0%
941
 
1.0%
882
 
2.0%
856
6.0%
832
 
2.0%
821
 
1.0%
802
 
2.0%
782
 
2.0%
771
 
1.0%

Shipping times
Real number (ℝ)

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.75
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:54.487263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.7242829
Coefficient of variation (CV)0.47378833
Kurtosis-1.0712955
Mean5.75
Median Absolute Deviation (MAD)2
Skewness-0.2815893
Sum575
Variance7.4217172
MonotonicityNot monotonic
2025-10-20T13:14:54.564383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
816
16.0%
714
14.0%
911
11.0%
410
10.0%
110
10.0%
610
10.0%
310
10.0%
58
8.0%
106
 
6.0%
25
 
5.0%
ValueCountFrequency (%)
110
10.0%
25
 
5.0%
310
10.0%
410
10.0%
58
8.0%
610
10.0%
714
14.0%
816
16.0%
911
11.0%
106
 
6.0%
ValueCountFrequency (%)
106
 
6.0%
911
11.0%
816
16.0%
714
14.0%
610
10.0%
58
8.0%
410
10.0%
310
10.0%
25
 
5.0%
110
10.0%

Shipping carriers
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Carrier B
43 
Carrier C
29 
Carrier A
28 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters900
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarrier B
2nd rowCarrier A
3rd rowCarrier B
4th rowCarrier C
5th rowCarrier A

Common Values

ValueCountFrequency (%)
Carrier B43
43.0%
Carrier C29
29.0%
Carrier A28
28.0%

Length

2025-10-20T13:14:54.655096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:54.716192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
carrier100
50.0%
b43
21.5%
c29
 
14.5%
a28
 
14.0%

Most occurring characters

ValueCountFrequency (%)
r300
33.3%
C129
14.3%
a100
 
11.1%
i100
 
11.1%
e100
 
11.1%
100
 
11.1%
B43
 
4.8%
A28
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r300
33.3%
C129
14.3%
a100
 
11.1%
i100
 
11.1%
e100
 
11.1%
100
 
11.1%
B43
 
4.8%
A28
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r300
33.3%
C129
14.3%
a100
 
11.1%
i100
 
11.1%
e100
 
11.1%
100
 
11.1%
B43
 
4.8%
A28
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r300
33.3%
C129
14.3%
a100
 
11.1%
i100
 
11.1%
e100
 
11.1%
100
 
11.1%
B43
 
4.8%
A28
 
3.1%

Shipping costs
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5481491
Minimum1.0134866
Maximum9.9298162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:54.819652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0134866
5-th percentile1.4055747
Q13.5402477
median5.320534
Q37.6016949
95-th percentile9.5745308
Maximum9.9298162
Range8.9163297
Interquartile range (IQR)4.0614472

Descriptive statistics

Standard deviation2.6513755
Coefficient of variation (CV)0.47788469
Kurtosis-1.1835666
Mean5.5481491
Median Absolute Deviation (MAD)2.231315
Skewness-0.053738287
Sum554.81491
Variance7.0297922
MonotonicityNot monotonic
2025-10-20T13:14:54.941552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9565721391
 
1.0%
9.7165747711
 
1.0%
8.0544792621
 
1.0%
1.7295685641
 
1.0%
3.8905479161
 
1.0%
4.4440988641
 
1.0%
3.8807633031
 
1.0%
2.3483387841
 
1.0%
3.4047338571
 
1.0%
7.1666452911
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
1.0134865661
1.0%
1.0194875711
1.0%
1.1942518651
1.0%
1.3110237561
1.0%
1.325274011
1.0%
1.4098010951
1.0%
1.454305311
1.0%
1.5129368371
1.0%
1.5326552741
1.0%
1.7295685641
1.0%
ValueCountFrequency (%)
9.9298162451
1.0%
9.8981405081
1.0%
9.7412916891
1.0%
9.7165747711
1.0%
9.705286791
1.0%
9.5676489211
1.0%
9.5372830611
1.0%
9.2359314371
1.0%
9.2281903171
1.0%
9.1605585351
1.0%

Supplier name
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Supplier 1
27 
Supplier 2
22 
Supplier 5
18 
Supplier 4
18 
Supplier 3
15 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupplier 3
2nd rowSupplier 3
3rd rowSupplier 1
4th rowSupplier 5
5th rowSupplier 1

Common Values

ValueCountFrequency (%)
Supplier 127
27.0%
Supplier 222
22.0%
Supplier 518
18.0%
Supplier 418
18.0%
Supplier 315
15.0%

Length

2025-10-20T13:14:55.048833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:55.120110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
supplier100
50.0%
127
 
13.5%
222
 
11.0%
518
 
9.0%
418
 
9.0%
315
 
7.5%

Most occurring characters

ValueCountFrequency (%)
p200
20.0%
S100
10.0%
u100
10.0%
l100
10.0%
i100
10.0%
e100
10.0%
r100
10.0%
100
10.0%
127
 
2.7%
222
 
2.2%
Other values (3)51
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p200
20.0%
S100
10.0%
u100
10.0%
l100
10.0%
i100
10.0%
e100
10.0%
r100
10.0%
100
10.0%
127
 
2.7%
222
 
2.2%
Other values (3)51
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p200
20.0%
S100
10.0%
u100
10.0%
l100
10.0%
i100
10.0%
e100
10.0%
r100
10.0%
100
10.0%
127
 
2.7%
222
 
2.2%
Other values (3)51
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p200
20.0%
S100
10.0%
u100
10.0%
l100
10.0%
i100
10.0%
e100
10.0%
r100
10.0%
100
10.0%
127
 
2.7%
222
 
2.2%
Other values (3)51
 
5.1%

Location
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Kolkata
25 
Mumbai
22 
Chennai
20 
Bangalore
18 
Delhi
15 

Length

Max length9
Median length7
Mean length6.84
Min length5

Characters and Unicode

Total characters684
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowMumbai
3rd rowMumbai
4th rowKolkata
5th rowDelhi

Common Values

ValueCountFrequency (%)
Kolkata25
25.0%
Mumbai22
22.0%
Chennai20
20.0%
Bangalore18
18.0%
Delhi15
15.0%

Length

2025-10-20T13:14:55.227900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:55.304843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kolkata25
25.0%
mumbai22
22.0%
chennai20
20.0%
bangalore18
18.0%
delhi15
15.0%

Most occurring characters

ValueCountFrequency (%)
a128
18.7%
n58
 
8.5%
l58
 
8.5%
i57
 
8.3%
e53
 
7.7%
o43
 
6.3%
h35
 
5.1%
K25
 
3.7%
t25
 
3.7%
k25
 
3.7%
Other values (9)177
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a128
18.7%
n58
 
8.5%
l58
 
8.5%
i57
 
8.3%
e53
 
7.7%
o43
 
6.3%
h35
 
5.1%
K25
 
3.7%
t25
 
3.7%
k25
 
3.7%
Other values (9)177
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a128
18.7%
n58
 
8.5%
l58
 
8.5%
i57
 
8.3%
e53
 
7.7%
o43
 
6.3%
h35
 
5.1%
K25
 
3.7%
t25
 
3.7%
k25
 
3.7%
Other values (9)177
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a128
18.7%
n58
 
8.5%
l58
 
8.5%
i57
 
8.3%
e53
 
7.7%
o43
 
6.3%
h35
 
5.1%
K25
 
3.7%
t25
 
3.7%
k25
 
3.7%
Other values (9)177
25.9%

Lead time
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:55.400436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median18
Q325
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8462513
Coefficient of variation (CV)0.5179304
Kurtosis-1.1745173
Mean17.08
Median Absolute Deviation (MAD)7.5
Skewness-0.32620585
Sum1708
Variance78.256162
MonotonicityNot monotonic
2025-10-20T13:14:55.996778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
189
 
9.0%
256
 
6.0%
286
 
6.0%
106
 
6.0%
246
 
6.0%
295
 
5.0%
265
 
5.0%
214
 
4.0%
134
 
4.0%
14
 
4.0%
Other values (19)45
45.0%
ValueCountFrequency (%)
14
4.0%
22
 
2.0%
33
3.0%
44
4.0%
53
3.0%
61
 
1.0%
72
 
2.0%
82
 
2.0%
92
 
2.0%
106
6.0%
ValueCountFrequency (%)
302
 
2.0%
295
5.0%
286
6.0%
273
3.0%
265
5.0%
256
6.0%
246
6.0%
233
3.0%
223
3.0%
214
4.0%

Production volumes
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.84
Minimum104
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:56.128285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile172.9
Q1352
median568.5
Q3797
95-th percentile953.1
Maximum985
Range881
Interquartile range (IQR)445

Descriptive statistics

Standard deviation263.04686
Coefficient of variation (CV)0.46324116
Kurtosis-1.2932782
Mean567.84
Median Absolute Deviation (MAD)230
Skewness-0.076547131
Sum56784
Variance69193.651
MonotonicityNot monotonic
2025-10-20T13:14:56.265390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1712
 
2.0%
7912
 
2.0%
8672
 
2.0%
6712
 
2.0%
4141
 
1.0%
1041
 
1.0%
2151
 
1.0%
5171
 
1.0%
7691
 
1.0%
9631
 
1.0%
Other values (86)86
86.0%
ValueCountFrequency (%)
1041
1.0%
1091
1.0%
1521
1.0%
1712
2.0%
1731
1.0%
1761
1.0%
1771
1.0%
1791
1.0%
1981
1.0%
2021
1.0%
ValueCountFrequency (%)
9851
1.0%
9711
1.0%
9641
1.0%
9631
1.0%
9551
1.0%
9531
1.0%
9371
1.0%
9341
1.0%
9291
1.0%
9211
1.0%

Manufacturing lead time
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.77
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:56.379007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9124303
Coefficient of variation (CV)0.60341437
Kurtosis-1.2944597
Mean14.77
Median Absolute Deviation (MAD)7.5
Skewness0.18499721
Sum1477
Variance79.431414
MonotonicityNot monotonic
2025-10-20T13:14:56.486447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
78
 
8.0%
287
 
7.0%
116
 
6.0%
55
 
5.0%
235
 
5.0%
185
 
5.0%
45
 
5.0%
104
 
4.0%
174
 
4.0%
294
 
4.0%
Other values (20)47
47.0%
ValueCountFrequency (%)
13
 
3.0%
23
 
3.0%
33
 
3.0%
45
5.0%
55
5.0%
63
 
3.0%
78
8.0%
83
 
3.0%
92
 
2.0%
104
4.0%
ValueCountFrequency (%)
302
 
2.0%
294
4.0%
287
7.0%
272
 
2.0%
262
 
2.0%
252
 
2.0%
243
3.0%
235
5.0%
221
 
1.0%
214
4.0%

Manufacturing costs
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.266693
Minimum1.0850686
Maximum99.466109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:56.607954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0850686
5-th percentile5.7820993
Q122.983299
median45.905622
Q368.621026
95-th percentile97.113967
Maximum99.466109
Range98.38104
Interquartile range (IQR)45.637726

Descriptive statistics

Standard deviation28.982841
Coefficient of variation (CV)0.61317683
Kurtosis-1.0923693
Mean47.266693
Median Absolute Deviation (MAD)23.065387
Skewness0.19149769
Sum4726.6693
Variance840.00509
MonotonicityNot monotonic
2025-10-20T13:14:56.733480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.279879241
 
1.0%
33.616768951
 
1.0%
30.688019351
 
1.0%
35.62474141
 
1.0%
92.06516061
 
1.0%
56.766475561
 
1.0%
1.085068571
 
1.0%
99.46610861
 
1.0%
11.423027141
 
1.0%
47.957601631
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
1.085068571
1.0%
1.5972227431
1.0%
1.9007622441
1.0%
4.4652784351
1.0%
5.6046908641
1.0%
5.791436631
1.0%
5.9306936461
1.0%
7.0578761471
1.0%
8.6930424261
1.0%
9.0058074291
1.0%
ValueCountFrequency (%)
99.46610861
1.0%
98.609957241
1.0%
97.829050111
1.0%
97.73059381
1.0%
97.121281751
1.0%
97.113581561
1.0%
96.527352791
1.0%
96.422820641
1.0%
95.332064551
1.0%
92.06516061
1.0%

Inspection results
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Pending
41 
Fail
36 
Pass
23 

Length

Max length7
Median length4
Mean length5.23
Min length4

Characters and Unicode

Total characters523
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowFail
5th rowFail

Common Values

ValueCountFrequency (%)
Pending41
41.0%
Fail36
36.0%
Pass23
23.0%

Length

2025-10-20T13:14:56.854093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:56.924952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pending41
41.0%
fail36
36.0%
pass23
23.0%

Most occurring characters

ValueCountFrequency (%)
n82
15.7%
i77
14.7%
P64
12.2%
a59
11.3%
s46
8.8%
e41
7.8%
g41
7.8%
d41
7.8%
F36
6.9%
l36
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n82
15.7%
i77
14.7%
P64
12.2%
a59
11.3%
s46
8.8%
e41
7.8%
g41
7.8%
d41
7.8%
F36
6.9%
l36
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n82
15.7%
i77
14.7%
P64
12.2%
a59
11.3%
s46
8.8%
e41
7.8%
g41
7.8%
d41
7.8%
F36
6.9%
l36
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n82
15.7%
i77
14.7%
P64
12.2%
a59
11.3%
s46
8.8%
e41
7.8%
g41
7.8%
d41
7.8%
F36
6.9%
l36
6.9%

Defect rates
Real number (ℝ)

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041128127
Minimum0.010009106
Maximum0.087812755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:57.021127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.010009106
5-th percentile0.013361461
Q10.021074243
median0.032979212
Q30.047769647
95-th percentile0.087812755
Maximum0.087812755
Range0.077803649
Interquartile range (IQR)0.026695405

Descriptive statistics

Standard deviation0.026224908
Coefficient of variation (CV)0.63763925
Kurtosis-0.54598465
Mean0.041128127
Median Absolute Deviation (MAD)0.012985624
Skewness0.93246059
Sum4.1128127
Variance0.00068774581
MonotonicityNot monotonic
2025-10-20T13:14:57.157322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0878127548721
 
21.0%
0.048540680261
 
1.0%
0.045805926191
 
1.0%
0.047466486211
 
1.0%
0.031455795231
 
1.0%
0.027791935121
 
1.0%
0.010009106191
 
1.0%
0.027098626911
 
1.0%
0.038446144791
 
1.0%
0.017273139281
 
1.0%
Other values (70)70
70.0%
ValueCountFrequency (%)
0.010009106191
1.0%
0.010125630891
1.0%
0.011737554951
1.0%
0.01210882131
1.0%
0.012193822241
1.0%
0.013422915631
1.0%
0.013623879891
1.0%
0.013744291
1.0%
0.014103475761
1.0%
0.014519722041
1.0%
ValueCountFrequency (%)
0.0878127548721
21.0%
0.049392552891
 
1.0%
0.049110959551
 
1.0%
0.048540680261
 
1.0%
0.048434565771
 
1.0%
0.047548008051
 
1.0%
0.047466486211
 
1.0%
0.046205460651
 
1.0%
0.045805926191
 
1.0%
0.045489196591
 
1.0%
Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Road
29 
Rail
28 
Air
26 
Sea
17 

Length

Max length4
Median length4
Mean length3.57
Min length3

Characters and Unicode

Total characters357
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoad
2nd rowRoad
3rd rowAir
4th rowRail
5th rowAir

Common Values

ValueCountFrequency (%)
Road29
29.0%
Rail28
28.0%
Air26
26.0%
Sea17
17.0%

Length

2025-10-20T13:14:57.269758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:57.337802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
road29
29.0%
rail28
28.0%
air26
26.0%
sea17
17.0%

Most occurring characters

ValueCountFrequency (%)
a74
20.7%
R57
16.0%
i54
15.1%
o29
 
8.1%
d29
 
8.1%
l28
 
7.8%
A26
 
7.3%
r26
 
7.3%
S17
 
4.8%
e17
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a74
20.7%
R57
16.0%
i54
15.1%
o29
 
8.1%
d29
 
8.1%
l28
 
7.8%
A26
 
7.3%
r26
 
7.3%
S17
 
4.8%
e17
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a74
20.7%
R57
16.0%
i54
15.1%
o29
 
8.1%
d29
 
8.1%
l28
 
7.8%
A26
 
7.3%
r26
 
7.3%
S17
 
4.8%
e17
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a74
20.7%
R57
16.0%
i54
15.1%
o29
 
8.1%
d29
 
8.1%
l28
 
7.8%
A26
 
7.3%
r26
 
7.3%
S17
 
4.8%
e17
 
4.8%

Routes
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Route A
43 
Route B
37 
Route C
20 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters700
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoute B
2nd rowRoute B
3rd rowRoute C
4th rowRoute A
5th rowRoute A

Common Values

ValueCountFrequency (%)
Route A43
43.0%
Route B37
37.0%
Route C20
20.0%

Length

2025-10-20T13:14:57.426410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:57.487287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
route100
50.0%
a43
21.5%
b37
 
18.5%
c20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
R100
14.3%
o100
14.3%
u100
14.3%
t100
14.3%
e100
14.3%
100
14.3%
A43
6.1%
B37
 
5.3%
C20
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R100
14.3%
o100
14.3%
u100
14.3%
t100
14.3%
e100
14.3%
100
14.3%
A43
6.1%
B37
 
5.3%
C20
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R100
14.3%
o100
14.3%
u100
14.3%
t100
14.3%
e100
14.3%
100
14.3%
A43
6.1%
B37
 
5.3%
C20
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R100
14.3%
o100
14.3%
u100
14.3%
t100
14.3%
e100
14.3%
100
14.3%
A43
6.1%
B37
 
5.3%
C20
 
2.9%

Costs
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.24578
Minimum103.91625
Maximum997.41345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:57.584057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum103.91625
5-th percentile134.04373
Q1318.77846
median520.43044
Q3763.07823
95-th percentile923.73036
Maximum997.41345
Range893.4972
Interquartile range (IQR)444.29978

Descriptive statistics

Standard deviation258.3017
Coefficient of variation (CV)0.48805622
Kurtosis-1.1693231
Mean529.24578
Median Absolute Deviation (MAD)239.5189
Skewness0.040144408
Sum52924.578
Variance66719.766
MonotonicityNot monotonic
2025-10-20T13:14:57.710427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187.75207551
 
1.0%
503.06557911
 
1.0%
141.92028181
 
1.0%
254.77615921
 
1.0%
923.44063171
 
1.0%
235.46123671
 
1.0%
134.36909691
 
1.0%
802.05631181
 
1.0%
505.55713421
 
1.0%
995.92946151
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
103.9162481
1.0%
110.36433521
1.0%
123.43702751
1.0%
126.72303341
1.0%
127.86181
1.0%
134.36909691
1.0%
141.92028181
1.0%
164.36652821
1.0%
169.27180141
1.0%
183.27289871
1.0%
ValueCountFrequency (%)
997.41345011
1.0%
996.7783151
1.0%
995.92946151
1.0%
990.07847251
1.0%
929.235291
1.0%
923.44063171
1.0%
882.19886351
1.0%
880.08098821
1.0%
879.35921771
1.0%
873.1296481
1.0%

Expected revenue
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22855.5
Minimum90.557466
Maximum87098.044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:57.831967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90.557466
5-th percentile865.77599
Q15489.6645
median13134.49
Q337071.66
95-th percentile63355.905
Maximum87098.044
Range87007.486
Interquartile range (IQR)31581.995

Descriptive statistics

Standard deviation22846.027
Coefficient of variation (CV)0.99958556
Kurtosis0.31085489
Mean22855.5
Median Absolute Deviation (MAD)10132.709
Skewness1.1251184
Sum2285550
Variance5.2194096 × 108
MonotonicityNot monotonic
2025-10-20T13:14:57.956290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55986.020441
 
1.0%
10924.833131
 
1.0%
90.557466341
 
1.0%
5076.557471
 
1.0%
4185.5870481
 
1.0%
249.8964741
 
1.0%
265.09163611
 
1.0%
18300.271751
 
1.0%
10307.639511
 
1.0%
62735.418281
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
90.557466341
1.0%
218.61889811
1.0%
249.8964741
1.0%
265.09163611
1.0%
810.81931281
1.0%
868.66844711
1.0%
944.5262341
1.0%
1533.2653381
1.0%
1690.8174041
1.0%
1924.074861
1.0%
ValueCountFrequency (%)
87098.043651
1.0%
87010.041581
1.0%
80386.100321
1.0%
79463.89361
1.0%
75145.148751
1.0%
62735.418281
1.0%
61862.960031
1.0%
56894.560361
1.0%
56362.066161
1.0%
55986.020441
1.0%

Revenue diff
Categorical

Constant 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
100 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0100
100.0%

Length

2025-10-20T13:14:58.065607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:58.118333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0100
100.0%

Most occurring characters

ValueCountFrequency (%)
0200
66.7%
.100
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0200
66.7%
.100
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0200
66.7%
.100
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0200
66.7%
.100
33.3%

stock_violation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
99 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
099
99.0%
11
 
1.0%

Length

2025-10-20T13:14:58.181480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T13:14:58.237624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
099
99.0%
11
 
1.0%

Most occurring characters

ValueCountFrequency (%)
099
99.0%
11
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
099
99.0%
11
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
099
99.0%
11
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
099
99.0%
11
 
1.0%

Stock levels (adj)
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.4
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-20T13:14:58.318865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117.75
median48
Q373.75
95-th percentile97.05
Maximum163
Range162
Interquartile range (IQR)56

Descriptive statistics

Standard deviation33.051842
Coefficient of variation (CV)0.66906562
Kurtosis-0.14462941
Mean49.4
Median Absolute Deviation (MAD)28.5
Skewness0.40364634
Sum4940
Variance1092.4242
MonotonicityNot monotonic
2025-10-20T13:14:58.446889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55
 
5.0%
904
 
4.0%
483
 
3.0%
1003
 
3.0%
103
 
3.0%
43
 
3.0%
312
 
2.0%
932
 
2.0%
12
 
2.0%
542
 
2.0%
Other values (55)71
71.0%
ValueCountFrequency (%)
12
 
2.0%
21
 
1.0%
43
3.0%
55
5.0%
61
 
1.0%
91
 
1.0%
103
3.0%
111
 
1.0%
121
 
1.0%
132
 
2.0%
ValueCountFrequency (%)
1631
 
1.0%
1003
3.0%
981
 
1.0%
972
2.0%
962
2.0%
951
 
1.0%
932
2.0%
921
 
1.0%
904
4.0%
891
 
1.0%

Interactions

2025-10-20T13:14:48.760246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:22.832412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.742705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.579768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.155446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.507047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.148405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.548743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.956807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.576226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.580867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.299130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.042694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.461856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.836906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.380201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.251848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.889209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:22.933099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.866584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.659964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.229571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.582696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.227690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.626713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.035057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.655432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.710675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.400132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.121950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.542875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.926068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.455359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.332870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.013550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.005239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.994528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.739498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.312232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.658432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.309228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.706924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.122385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.728467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.830213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.479244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.203994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.618513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.017743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.533094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.419104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.147214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.086613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.133279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.821444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.388923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.741187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.387856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.800177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.199525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.809465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.958291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.557282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.285922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.706430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.109535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.611532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.499780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.276531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.160866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.269921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.900831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.465446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.826233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.471472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.880284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.274350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.886832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.078415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.631548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.365936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.781279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.196380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.694485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.580455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.395655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.235327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.406487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.983379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.542548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.908909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.550345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.956199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.351962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.999110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.197981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.709453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.446962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.863776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.281188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.776899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.658293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.523065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.315217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.540859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.071667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.624854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.992880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.634000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.041123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.433326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.118487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.327953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.790097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.529587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.944891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.372253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.859857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.736895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.664001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.400526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.666131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.151312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.701213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.081535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.716797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.132431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.508354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.236451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.460036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.871718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.610388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.024941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.465138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.936443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.814490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.789895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.519894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.784305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.226537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.776977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.168191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.796183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.210080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.853935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.337357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.545904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.952495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.693971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.105609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.554451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.009852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.892103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:49.925345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.634411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:25.922450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.308622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.854912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.249877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.873398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.297266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:34.925986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.439233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.619990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.038805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.777051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.181759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.637395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.088184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.968303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.065159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.742311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.002438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.389452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.939111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.335696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.960831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.378076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.004307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.557579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.706105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.124886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.863323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.260081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.732465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.274776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.052995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.199241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.847998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.080737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.467491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.014841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.420756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.040320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.458636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.080673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.661191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.789646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.205222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:41.946217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.340291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.819505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.348442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.137036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.337590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:23.960363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.163598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.725983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.106079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.513605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.130313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.538229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.163816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.775350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.873961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.291413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.031792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.419994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:44.921222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.423467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.219549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.477432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.072581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.240839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.810761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.183233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.594146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.210934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.618674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.245467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:36.898582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:38.954834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.710144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.117217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.498851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.012069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.908607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.302240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.618904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.194333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.331721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.902006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.270389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.689784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.302777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.709383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.340181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.019477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.049901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.799813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.211642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.586541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.113335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:46.993438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.397493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.755280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.299917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.416877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:27.978395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.345422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:30.984975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.377676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.786308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.415094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.126093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.127815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.875142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.289916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.664925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.197106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.078086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.481593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:50.890202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:24.445079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:26.493341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:28.065443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:29.419123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:31.062478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:32.459647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:33.866947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:35.491370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:37.451771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:39.210102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:40.950826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:42.370289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:43.747270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:45.284704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:47.158249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T13:14:48.625263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-20T13:14:58.575446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AvailabilityCostsCustomer demographicsDefect ratesExpected revenueInspection resultsLead timeLead timesLocationManufacturing costsManufacturing lead timeNumber of products soldOrder quantitiesPriceProduct typeProduction volumesRevenue generatedRoutesShipping carriersShipping costsShipping timesStock levelsStock levels (adj)Supplier nameTransportation modesstock_violation
Availability1.000-0.0420.1150.0090.0740.158-0.1860.1690.0000.1420.0770.0680.1280.0000.0000.0710.0740.1490.171-0.056-0.039-0.057-0.0650.1760.0000.000
Costs-0.0421.0000.081-0.101-0.0170.0000.0490.2460.091-0.025-0.094-0.0350.1530.0960.000-0.078-0.0160.1170.2230.041-0.023-0.023-0.0380.0000.2460.000
Customer demographics0.1150.0811.0000.0000.1130.1400.0000.0000.0000.0000.0000.1470.2020.0000.1890.0000.1130.0000.0000.0000.0000.0900.0000.0300.0000.060
Defect rates0.009-0.1010.0001.0000.2420.1400.0070.2520.000-0.092-0.0280.1600.0950.1340.082-0.0090.2420.0190.1610.0160.103-0.006-0.0230.1420.1030.000
Expected revenue0.074-0.0170.1130.2421.0000.0000.1540.0410.000-0.137-0.2220.6310.1060.7100.2270.0281.0000.2100.0000.0160.1020.1250.1100.0000.0000.000
Inspection results0.1580.0000.1400.1400.0001.0000.0000.0000.0000.0000.2570.1870.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.0000.2790.0000.000
Lead time-0.1860.0490.0000.0070.1540.0001.0000.0050.000-0.1240.0150.046-0.0860.1550.1610.1880.1540.0000.1040.008-0.0520.0530.0260.0000.0360.106
Lead times0.1690.2460.0000.2520.0410.0000.0051.0000.090-0.020-0.011-0.0310.1190.0380.114-0.1370.0430.2410.054-0.118-0.0280.0790.0630.0000.0360.000
Location0.0000.0910.0000.0000.0000.0000.0000.0901.0000.1750.0000.1930.0340.1250.0000.0880.0000.0000.0000.0610.0000.0000.0000.0000.0630.075
Manufacturing costs0.142-0.0250.000-0.092-0.1370.000-0.124-0.0200.1751.000-0.1370.035-0.035-0.1870.2480.079-0.1370.0000.0000.0190.0370.0540.0790.1640.0000.000
Manufacturing lead time0.077-0.0940.000-0.028-0.2220.2570.015-0.0110.000-0.1371.000-0.0770.104-0.2910.0810.195-0.2220.0000.000-0.0070.020-0.057-0.0490.0000.0000.000
Number of products sold0.068-0.0350.1470.1600.6310.1870.046-0.0310.1930.035-0.0771.0000.0270.0070.1610.1670.6300.0000.0000.0500.0900.0410.0230.1750.0000.000
Order quantities0.1280.1530.2020.0950.1060.000-0.0860.1190.034-0.0350.1040.0271.0000.1020.137-0.0640.1060.0860.000-0.0040.003-0.087-0.0810.0380.0000.000
Price0.0000.0960.0000.1340.7100.0000.1550.0380.125-0.187-0.2910.0070.1021.0000.260-0.1170.7110.0890.0000.0450.0750.0890.0830.0000.1080.160
Product type0.0000.0000.1890.0820.2270.0000.1610.1140.0000.2480.0810.1610.1370.2601.0000.0000.2270.0000.1070.1550.0000.0940.0000.1990.0000.000
Production volumes0.071-0.0780.000-0.0090.0280.0000.188-0.1370.0880.0790.1950.167-0.064-0.1170.0001.0000.0270.1380.000-0.096-0.0640.0180.0050.1550.0000.000
Revenue generated0.074-0.0160.1130.2421.0000.0000.1540.0430.000-0.137-0.2220.6300.1060.7110.2270.0271.0000.2100.0000.0150.1020.1250.1110.0000.0000.000
Routes0.1490.1170.0000.0190.2100.0000.0000.2410.0000.0000.0000.0000.0860.0890.0000.1380.2101.0000.0000.0000.0810.0000.0000.0000.0000.000
Shipping carriers0.1710.2230.0000.1610.0000.1580.1040.0540.0000.0000.0000.0000.0000.0000.1070.0000.0000.0001.0000.0000.1420.2020.0000.0000.1450.000
Shipping costs-0.0560.0410.0000.0160.0160.0000.008-0.1180.0610.019-0.0070.050-0.0040.0450.155-0.0960.0150.0000.0001.0000.0620.0530.0250.0000.0000.000
Shipping times-0.039-0.0230.0000.1030.1020.000-0.052-0.0280.0000.0370.0200.0900.0030.0750.000-0.0640.1020.0810.1420.0621.000-0.102-0.0860.0000.1570.000
Stock levels-0.057-0.0230.090-0.0060.1250.0000.0530.0790.0000.054-0.0570.041-0.0870.0890.0940.0180.1250.0000.2020.053-0.1021.0000.9860.0000.0000.000
Stock levels (adj)-0.065-0.0380.000-0.0230.1100.0000.0260.0630.0000.079-0.0490.023-0.0810.0830.0000.0050.1110.0000.0000.025-0.0860.9861.0000.0000.0000.969
Supplier name0.1760.0000.0300.1420.0000.2790.0000.0000.0000.1640.0000.1750.0380.0000.1990.1550.0000.0000.0000.0000.0000.0000.0001.0000.2050.000
Transportation modes0.0000.2460.0000.1030.0000.0000.0360.0360.0630.0000.0000.0000.0000.1080.0000.0000.0000.0000.1450.0000.1570.0000.0000.2051.0000.000
stock_violation0.0000.0000.0600.0000.0000.0000.1060.0000.0750.0000.0000.0000.0000.1600.0000.0000.0000.0000.0000.0000.0000.0000.9690.0000.0001.000

Missing values

2025-10-20T13:14:51.121461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-20T13:14:51.365706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Product typeSKUPriceAvailabilityNumber of products soldRevenue generatedCustomer demographicsStock levelsLead timesOrder quantitiesShipping timesShipping carriersShipping costsSupplier nameLocationLead timeProduction volumesManufacturing lead timeManufacturing costsInspection resultsDefect ratesTransportation modesRoutesCostsExpected revenueRevenue diffstock_violationStock levels (adj)
0haircareSKU069.8080065580255986.020445Non-binary58.07964Carrier B2.956572Supplier 3Mumbai292152946.279879Pending0.087813RoadRoute B187.75207555986.0204450.0058
1skincareSKU114.8435239573610924.833130Female53.030372Carrier A9.716575Supplier 3Mumbai235173033.616769Pending0.048541RoadRoute B503.06557910924.8331300.0053
2haircareSKU211.31968334890.557466Unknown1.010882Carrier B8.054479Supplier 1Mumbai129712730.688019Pending0.045806AirRoute C141.92028290.5574660.001
3skincareSKU361.16334368835076.557470Non-binary23.013596Carrier C1.729569Supplier 5Kolkata249371835.624741Fail0.047466RailRoute A254.7761595076.5574700.0023
4skincareSKU44.805496268714185.587048Non-binary5.03568Carrier A3.890548Supplier 1Delhi5414392.065161Fail0.031456AirRoute A923.4406324185.5870480.005
5haircareSKU51.69997687147249.896474Non-binary90.027663Carrier B4.444099Supplier 4Bangalore101041756.766476Fail0.027792RoadRoute A235.461237249.8964740.0090
6skincareSKU64.0783334865265.091636Male11.015588Carrier C3.880763Supplier 3Kolkata14314241.085069Pending0.010009SeaRoute A134.369097265.0916360.0011
7cosmeticsSKU742.9583845942618300.271747Female93.017111Carrier B2.348339Supplier 4Bangalore22564199.466109Fail0.087813RoadRoute C802.05631218300.2717470.0093
8cosmeticsSKU868.7175977815010307.639512Female5.010157Carrier C3.404734Supplier 4Mumbai13769811.423027Pending0.027099SeaRoute B505.55713410307.6395120.005
9skincareSKU964.0157333598062735.418282Unknown14.027831Carrier A7.166645Supplier 2Chennai299632347.957602Pending0.038446RailRoute B995.92946162735.4182820.0014
Product typeSKUPriceAvailabilityNumber of products soldRevenue generatedCustomer demographicsStock levelsLead timesOrder quantitiesShipping timesShipping carriersShipping costsSupplier nameLocationLead timeProduction volumesManufacturing lead timeManufacturing costsInspection resultsDefect ratesTransportation modesRoutesCostsExpected revenueRevenue diffstock_violationStock levels (adj)
90skincareSKU9013.881914563204442.212320Non-binary66.018967Carrier B7.674431Supplier 3Bangalore8585885.675963Pass0.012194RailRoute B990.0784734442.2123200.0066
91cosmeticsSKU9162.1119659091656894.560365Male98.022857Carrier B7.471514Supplier 4Delhi52072839.772883Pending0.087813RailRoute B996.77831556894.5603650.0098
92cosmeticsSKU9247.7142334427613169.128329Male90.025108Carrier B4.469500Supplier 2Mumbai46712962.612690Pass0.087813RailRoute B230.09278313169.1283290.0090
93haircareSKU9369.290831881147899.154734Unknown63.017661Carrier C7.006432Supplier 4Chennai218242035.633652Fail0.041658AirRoute A823.5238467899.1547340.0063
94cosmeticsSKU943.037689979872998.198771Unknown77.026729Carrier B6.942946Supplier 2Delhi129081460.387379Pass0.014636RailRoute B846.6652572998.1987710.0077
95haircareSKU9577.9039276567252351.439091Unknown15.014269Carrier B8.630339Supplier 4Mumbai184502658.890686Pending0.012109AirRoute A778.86424152351.4390910.0015
96cosmeticsSKU9624.423131293247698.424766Non-binary67.02323Carrier C5.352878Supplier 3Mumbai286482817.803756Pending0.038720RoadRoute A188.7421417913.0945800.0067
97haircareSKU973.5261115662218.618898Male46.01949Carrier A7.904846Supplier 4Mumbai105351365.765156Fail0.033762RoadRoute A540.132423218.6188980.0046
98skincareSKU9819.7546054391318035.954243Female53.01277Carrier B1.409801Supplier 5Chennai2858195.604691Pending0.029081RailRoute A882.19886418035.9542430.0053
99haircareSKU9968.5178331762742960.681102Unknown55.08596Carrier B1.311024Supplier 2Chennai29921238.072899Fail0.087813RailRoute B210.74300942960.6811020.0055